A method and device for intelligently controlling power consumption of a reinforced computer

Abstract

The present invention relates to power consumption intelligent analysis and processing technology, and in particular to a method and device for intelligently controlling power consumption of a reinforced computer. The method comprises the following steps: obtaining the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware through the reinforced computer and performing power consumption monitoring analysis and hardware power consumption energy ratio analysis to obtain the power consumption energy ratio of the reinforced computer hardware; performing workload evaluation analysis on the reinforced computer to obtain the workload degree of the reinforced computer hardware; comparing and judging the hardware power consumption mode of the reinforced computer according to the workload degree of the reinforced computer hardware to obtain the judgment result of the power consumption mode of the reinforced computer hardware; generating a corresponding intelligent control strategy according to the judgment result of the power consumption mode of the reinforced computer hardware to perform the corresponding hardware power consumption intelligent control work. The present invention can realize intelligent monitoring and control of the power consumption of reinforced computers.

Description

Technical Field

[0001] The present invention relates to a power consumption intelligent analysis and processing technology, and in particular to a method and device for intelligently controlling power consumption of a reinforced computer. Background Art

[0002] In the current digital age, hardened computers have become an indispensable tool in daily life and work. With the continuous improvement of hardened computer performance and the continuous expansion of application scenarios, the power consumption of hardened computers has become an increasingly serious problem. High power consumption not only increases the cost of use, but also leads to heat dissipation problems and energy waste, which has a negative impact on the environment. At present, common power consumption control methods mainly include static power consumption management and dynamic power consumption management. Among them, static power consumption management reduces power consumption by adjusting the hardware configuration and settings of the computer, such as adjusting the processor frequency, shutting down unnecessary peripherals, etc., while dynamic power consumption management realizes dynamic adjustment of power consumption through software control and intelligent algorithms. This method can adjust the power consumption level in real time according to the computer load and user behavior to achieve the best balance between performance and power consumption. However, existing power consumption analysis methods often lack flexibility and accuracy, and cannot fully consider the power consumption characteristics and mutual influence of various hardware components inside the hardened computer. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and device for intelligently controlling power consumption of a reinforced computer to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for intelligently controlling power consumption of a reinforced computer comprises the following steps:

[0005] Step S1: obtaining the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware through the reinforced computer, and performing power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware to obtain the power consumption data of the reinforced computer hardware;

[0006] Step S2: Based on the power consumption data of the reinforced computer hardware, the hardware power consumption energy ratio of the reinforced computer is analyzed to obtain the power consumption energy ratio of the reinforced computer hardware; based on the power consumption data of the reinforced computer hardware and the power consumption energy ratio of the reinforced computer hardware, the workload evaluation analysis of the reinforced computer is performed to obtain the workload degree of the reinforced computer hardware;

[0007] Step S3: comparing and judging the hardware power consumption mode of the reinforced computer according to the workload of the reinforced computer hardware to obtain a judgment result of the hardware power consumption mode of the reinforced computer, wherein the judgment result of the hardware power consumption mode of the reinforced computer includes a high load power consumption mode and a low load power consumption mode;

[0008] Step S4: when it is determined that the power consumption mode of the hardware of the reinforced computer is a high-load power consumption mode, the power consumption intelligent control analysis of the reinforced computer is performed based on the power consumption data of the hardware of the reinforced computer, and a high-load power consumption mode intelligent control strategy is generated to execute the corresponding hardware high-load power consumption intelligent control work;

[0009] Step S5: When it is determined that the hardware power consumption mode of the reinforced computer is in low-load power consumption mode, the hardware usage status of the reinforced computer is analyzed to obtain the hardware usage status data of the reinforced computer; an intelligent sleep strategy is designed according to the hardware usage status data of the reinforced computer to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0010] The present invention firstly evaluates and analyzes the hardware utilization efficiency of the reinforced computer, and can obtain the hardware utilization efficiency of the reinforced computer. This evaluation and analysis process can help discover the utilization efficiency of hardware resources, guide subsequent resource allocation and performance optimization work, thereby improving the overall work efficiency and performance of the reinforced computer. At the same time, by using the reinforced computer to obtain hardware operating temperature data, the operating temperature of each hardware component of the computer can be monitored in real time. This step helps to understand the thermal operation of the reinforced computer, and timely discover and handle hardware temperature anomalies, thereby providing basic data guarantee for subsequent reinforced computer power consumption monitoring. In addition, by performing power consumption monitoring and analysis on the reinforced computer according to the hardware utilization efficiency of the reinforced computer and the hardware operating temperature of the reinforced computer, this analysis can help evaluate the energy consumption of the reinforced computer, guide subsequent intelligent control of energy conservation and emission reduction and resource management, thereby improving the flexibility and accuracy of the reinforced computer hardware power consumption monitoring. Secondly, by analyzing the hardware power consumption energy ratio of each hardware in the reinforced computer based on the power consumption data of the reinforced computer hardware, this analysis process can reveal the power consumption contribution ratio of different hardware in the entire reinforced computer, thereby helping to identify the main sources of energy consumption. By understanding the power consumption energy ratio of each hardware, energy efficiency improvement strategies can be formulated in a targeted manner, focusing on optimizing high-power consumption components, thereby achieving overall energy consumption reduction. By performing workload evaluation and analysis on each hardware in the reinforced computer based on the power consumption data of the reinforced computer hardware and the power consumption energy ratio of the reinforced computer hardware, the workload level of the reinforced computer hardware can be evaluated and analyzed. The purpose of this step is to quantify the power consumption performance of the hardware under different workloads and provide a basis for the dynamic power consumption management of the reinforced computer. By evaluating the workload level of the hardware, the resource allocation of the reinforced computer can be optimized, so that the power consumption characteristics and mutual influence of each hardware component inside the reinforced computer can be fully considered. Then, the workload of the reinforced computer hardware is compared and judged by the preset hardware workload threshold. If the workload of a certain hardware is greater than or equal to the preset threshold, the power consumption mode of the hardware can be determined as a high-load power consumption mode, which can help identify the high power consumption state of each hardware in the reinforced computer, so as to take timely measures to adjust and optimize, and ensure the stability and efficiency of the reinforced computer. When the workload of the reinforced computer hardware is less than the preset hardware workload threshold, the power consumption mode of the hardware can be determined as a low-load power consumption mode, which can adopt corresponding energy-saving strategies to reduce power consumption, such as reducing voltage frequency, adjusting performance mode or shutting down some hardware functions, and at the same time can minimize the energy efficiency of the reinforced computer to extend the service life of the hardware and reduce energy consumption and operating costs.Next, when it is determined that the power consumption mode of the reinforced computer hardware is a high-load power consumption mode, the power consumption intelligent control analysis of each hardware in the reinforced computer is performed based on the power consumption data of the reinforced computer hardware to generate an intelligent control strategy for the high-load power consumption mode. The key to this step is to comprehensively consider the results of dynamic working frequency adjustment and hardware collaborative optimization to formulate an intelligent control strategy, thereby realizing intelligent management and control of the high-load power consumption mode, so that the reinforced computer can maintain a stable power consumption level under high load conditions, while maximizing the performance and energy efficiency of the reinforced computer. Finally, when it is determined that the power consumption mode of the reinforced computer hardware is a low-load power consumption mode, the hardware usage status of each hardware in the reinforced computer is analyzed. This step can comprehensively evaluate the activity level, working status and resource utilization of each hardware, thereby obtaining the hardware usage status data of the reinforced computer. This analysis can not only fully understand the working conditions of each hardware, but also provide data support for the subsequent intelligent sleep trigger condition design, thereby realizing accurate management and control of hardware under low load conditions. In addition, an intelligent sleep strategy response is designed for each hardware in the reinforced computer based on the hardware usage data of the reinforced computer. This step can formulate corresponding intelligent sleep strategies according to different sleep trigger conditions. These strategies may include targeted adjustments to the hardware's working mode, shutting down or reducing unnecessary power consumption modules, optimizing power management and other measures to minimize the energy consumption of the hardware under low load conditions, thereby achieving intelligent optimization and management of the power consumption of the reinforced computer.

[0011] Preferably, the present invention further provides an intelligent power consumption control device for a reinforced computer, which is used to execute the intelligent power consumption control method for a reinforced computer as described above, and the intelligent power consumption control device for a reinforced computer comprises:

[0012] The power consumption monitoring module of the reinforced computer hardware is used to obtain the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware, and perform power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware, so as to obtain the power consumption data of the reinforced computer hardware;

[0013] A reinforcement computer workload assessment module is used to analyze the hardware power consumption energy ratio of the reinforcement computer based on the power consumption data of the reinforcement computer hardware, so as to obtain the power consumption energy ratio of the reinforcement computer hardware; based on the power consumption data of the reinforcement computer hardware and the power consumption energy ratio of the reinforcement computer hardware, a workload assessment analysis is performed on the reinforcement computer, so as to obtain the degree of the reinforcement computer hardware workload;

[0014] The reinforcement computer hardware power consumption mode judgment module is used to compare and judge the hardware power consumption mode of the reinforcement computer according to the workload of the reinforcement computer hardware to obtain the reinforcement computer hardware power consumption mode judgment result, wherein the reinforcement computer hardware power consumption mode judgment result includes a high load power consumption mode and a low load power consumption mode;

[0015] The hardware high-load power consumption mode control module is used to determine that when the power consumption mode judgment result of the reinforced computer hardware is a high-load power consumption mode, perform power consumption intelligent control analysis on the reinforced computer based on the power consumption data of the reinforced computer hardware, generate a high-load power consumption mode intelligent control strategy, and execute the corresponding hardware high-load power consumption intelligent control work;

[0016] The hardware low-load power consumption mode control module is used to determine that when the hardware power consumption mode of the reinforced computer is judged as the low-load power consumption mode, the hardware usage status of the reinforced computer is analyzed to obtain the hardware usage status data of the reinforced computer; an intelligent sleep strategy is designed according to the hardware usage status data of the reinforced computer to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0017] In summary, the present invention provides an intelligent power consumption control device for a reinforced computer, which is composed of a reinforced computer hardware power consumption monitoring module, a reinforced computer workload evaluation module, a reinforced computer hardware power consumption mode judgment module, a hardware high-load power consumption mode control module, and a hardware low-load power consumption mode control module. It can implement any of the intelligent power consumption control methods for a reinforced computer described in the present invention, and is used to combine the operations between computer programs running on each module to implement any of the intelligent power consumption control methods for a reinforced computer. The internal structures of the device cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient intelligent power consumption control process, thereby simplifying the operating procedures of the intelligent power consumption control device for a reinforced computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0019] Figure 1 A schematic diagram of the steps of the intelligent power consumption control method for strengthening a computer according to the present invention;

[0020] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0021] Figure 3 for Figure 2 Detailed step flow chart of step S14 in FIG. DETAILED DESCRIPTION

[0022] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0024] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0025] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for intelligently controlling power consumption of a reinforced computer, the method comprising the following steps:

[0026] Step S1: obtaining the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware through the reinforced computer, and performing power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware to obtain the power consumption data of the reinforced computer hardware;

[0027] Step S2: Based on the power consumption data of the reinforced computer hardware, the hardware power consumption energy ratio of the reinforced computer is analyzed to obtain the power consumption energy ratio of the reinforced computer hardware; based on the power consumption data of the reinforced computer hardware and the power consumption energy ratio of the reinforced computer hardware, the workload evaluation analysis of the reinforced computer is performed to obtain the workload degree of the reinforced computer hardware;

[0028] Step S3: comparing and judging the hardware power consumption mode of the reinforced computer according to the workload of the reinforced computer hardware to obtain a judgment result of the hardware power consumption mode of the reinforced computer, wherein the judgment result of the hardware power consumption mode of the reinforced computer includes a high load power consumption mode and a low load power consumption mode;

[0029] Step S4: when it is determined that the power consumption mode of the hardware of the reinforced computer is a high-load power consumption mode, the power consumption intelligent control analysis of the reinforced computer is performed based on the power consumption data of the hardware of the reinforced computer, and a high-load power consumption mode intelligent control strategy is generated to execute the corresponding hardware high-load power consumption intelligent control work;

[0030] Step S5: When it is determined that the hardware power consumption mode of the reinforced computer is in low-load power consumption mode, the hardware usage status of the reinforced computer is analyzed to obtain the hardware usage status data of the reinforced computer; an intelligent sleep strategy is designed according to the hardware usage status data of the reinforced computer to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0031] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a flow chart of the steps of the method for intelligently controlling power consumption of a computer for strengthening the present invention. In this example, the method for intelligently controlling power consumption of a computer for strengthening the present invention includes the following steps:

[0032] Step S1: obtaining the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware through the reinforced computer, and performing power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware to obtain the power consumption data of the reinforced computer hardware;

[0033] The embodiment of the present invention performs a statistical analysis on the use efficiency of each hardware in the reinforced computer (including the motherboard (including CPU + chipset), SSD, cooling fan, LCD screen, battery), and evaluates the effective utilization of the corresponding hardware in combination with the hardware use frequency and hardware activity status data obtained by the analysis, thereby obtaining the use efficiency of the reinforced computer hardware. At the same time, the surface temperature of each hardware in the reinforced computer is monitored in real time by using a temperature sensor to monitor the surface temperature of the reinforced computer hardware in real time, and the internal temperature of each hardware in the reinforced computer is monitored in real time by using thermal imaging technology or infrared cameras and other equipment to monitor the temperature generated inside the reinforced computer hardware in real time. At the same time, the working temperature of each hardware in the reinforced computer under an ideal working state is determined based on the monitored surface temperature and internal temperature. Secondly, the corresponding heat dissipation efficiency is obtained through statistical analysis, and the hardware temperature of the reinforced computer hardware corresponding to the determined ideal working temperature is optimized during the heat dissipation process to obtain the actual working temperature of each hardware in the reinforced computer after heat dissipation treatment under normal circumstances, thereby obtaining the working temperature of the reinforced computer hardware. Then, by combining the analyzed usage efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware, the corresponding hardware in the reinforced computer is evaluated and analyzed, so as to fully consider the influence of the efficiency and temperature of each hardware on its loss to analyze the maximum loss of each hardware under actual working conditions, and by taking the maximum loss of each hardware under actual working conditions as the power consumption of the corresponding hardware in the reinforced computer, it is used to further improve the energy efficiency performance and energy-saving effect of the reinforced computer, and finally obtain the power consumption data of the reinforced computer hardware.

[0034] Step S2: Based on the power consumption data of the reinforced computer hardware, the hardware power consumption energy ratio of the reinforced computer is analyzed to obtain the power consumption energy ratio of the reinforced computer hardware; based on the power consumption data of the reinforced computer hardware and the power consumption energy ratio of the reinforced computer hardware, the workload evaluation analysis of the reinforced computer is performed to obtain the workload degree of the reinforced computer hardware;

[0035] The embodiment of the present invention uses an energy conversion analysis method to analyze the corresponding hardware in the reinforced computer by combining the power consumption data of the reinforced computer hardware obtained by analysis, so as to convert the power consumption of each hardware of the reinforced computer into the power consumption energy consumed by each hardware in the reinforced computer, and statistically calculate the energy contribution ratio of each hardware in the overall power consumption, thereby obtaining the power consumption energy ratio of the reinforced computer hardware. Secondly, by combining the power consumption data of the reinforced computer hardware obtained by analysis and the power consumption energy ratio of the reinforced computer hardware, the corresponding hardware in the reinforced computer is analyzed to explore and analyze the influence correlation between the power consumption of the hardware and the workload, and then, by combining the influence correlation obtained by analysis, a load evaluation algorithm is used to evaluate and analyze the corresponding hardware in the reinforced computer, so as to fully consider the power consumption characteristics and mutual influence relationship of each hardware component inside the reinforced computer to quantify the power consumption performance of the hardware under different workloads, and finally obtain the workload degree of the reinforced computer hardware.

[0036] Step S3: comparing and judging the hardware power consumption mode of the reinforced computer according to the workload of the reinforced computer hardware to obtain a judgment result of the hardware power consumption mode of the reinforced computer, wherein the judgment result of the hardware power consumption mode of the reinforced computer includes a high load power consumption mode and a low load power consumption mode;

[0037] The embodiment of the present invention compares and judges the workload degree of the reinforced computer hardware obtained by evaluation and calculation by using a preset hardware workload threshold. If the workload degree of the reinforced computer hardware is greater than or equal to the preset hardware workload threshold, it means that the workload degree of each reinforced computer hardware corresponding to the workload degree of the reinforced computer hardware is large, and the hardware power consumption mode of the reinforced computer corresponding to the workload degree of the reinforced computer hardware is judged as a high-load power consumption mode. If it is judged that the workload degree of the reinforced computer hardware is less than the preset hardware workload threshold, it means that the workload degree of each reinforced computer hardware corresponding to the workload degree of the reinforced computer hardware is small, and the hardware power consumption mode of the reinforced computer corresponding to the workload degree of the reinforced computer hardware is judged as a low-load power consumption mode, and finally the judgment result of the power consumption mode of the reinforced computer hardware is obtained.

[0038] Step S4: when it is determined that the power consumption mode of the hardware of the reinforced computer is a high-load power consumption mode, the power consumption intelligent control analysis of the reinforced computer is performed based on the power consumption data of the hardware of the reinforced computer, and a high-load power consumption mode intelligent control strategy is generated to execute the corresponding hardware high-load power consumption intelligent control work;

[0039] The embodiment of the present invention determines that the power consumption mode of the reinforced computer hardware is a high-load power consumption mode, and then designs the load power consumption targets for the corresponding hardware in the reinforced computer by combining the analyzed power consumption data of the reinforced computer hardware, so as to analyze the power consumption of each hardware according to the current high-load state, set a reasonable power consumption target for each hardware, and generate various measures that can adjust the operating frequency of the hardware device according to the current load power consumption target and current working state of each hardware according to the designed power consumption target, including dynamically adjusting the operating frequency of the processor, CPU and GPU, hard disk, etc., and generating measures that can optimize the operating frequency according to the current load power consumption target and current working state of each hardware. Various measures to optimize the working status of each hardware are taken, including optimizing the hard disk rotation speed, optimizing memory usage, optimizing voltage supply and other measures. Then, the dynamic working frequency adjustment measures and dynamic collaborative optimization measures obtained by integration analysis are used to intelligently control and analyze the corresponding hardware in the reinforced computer to determine the working mode and adjustment plan of each hardware device under high load conditions, and enable it to realize intelligent control of hardware high-load power consumption. Finally, an intelligent control strategy for high-load power consumption mode is generated to perform corresponding hardware high-load power consumption intelligent control work, including dynamically adjusting the working frequency of the processor, CPU and GPU, adjusting the hard disk rotation speed, reducing the peripheral power supply voltage, optimizing memory usage and other measures.

[0040] Step S5: When it is determined that the hardware power consumption mode of the reinforced computer is in low-load power consumption mode, the hardware usage status of the reinforced computer is analyzed to obtain the hardware usage status data of the reinforced computer; an intelligent sleep strategy is designed according to the hardware usage status data of the reinforced computer to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0041] When the embodiment of the present invention determines that the result of the judgment of the power consumption mode of the reinforced computer hardware is a low-load power consumption mode, the use status of each hardware in the reinforced computer is analyzed to monitor and analyze the activity level, working status and resource utilization of each hardware in the reinforced computer, thereby obtaining the use status data of the reinforced computer hardware. Secondly, by combining the analyzed use status data of the reinforced computer hardware, the trigger design of the sleep mode of each corresponding hardware in the reinforced computer is performed, so as to set the trigger conditions such as the threshold of the corresponding hardware usage rate and the device idle time threshold based on the idle time of the hardware, the task completion status, the energy consumption level and other factors, and then, by using the set trigger conditions, the corresponding hardware in the reinforced computer is responded to and processed, so as to formulate the corresponding hardware intelligent sleep strategy that meets the trigger conditions in the reinforced computer according to different sleep trigger conditions, including targeted adjustment of the hardware working mode, closing or reducing unnecessary power consumption modules, optimizing power management and other measures, and ensuring that the energy consumption of the hardware in the low-load state is reduced to the greatest extent, and finally generating a low-load power consumption mode intelligent sleep strategy to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0042] The present invention firstly evaluates and analyzes the hardware utilization efficiency of the reinforced computer, and can obtain the hardware utilization efficiency of the reinforced computer. This evaluation and analysis process can help discover the utilization efficiency of hardware resources, guide subsequent resource allocation and performance optimization work, thereby improving the overall work efficiency and performance of the reinforced computer. At the same time, by using the reinforced computer to obtain hardware operating temperature data, the operating temperature of each hardware component of the computer can be monitored in real time. This step helps to understand the thermal operation of the reinforced computer, and timely discover and handle hardware temperature anomalies, thereby providing basic data guarantee for subsequent reinforced computer power consumption monitoring. In addition, by performing power consumption monitoring and analysis on the reinforced computer according to the hardware utilization efficiency of the reinforced computer and the hardware operating temperature of the reinforced computer, this analysis can help evaluate the energy consumption of the reinforced computer, guide subsequent intelligent control of energy conservation and emission reduction and resource management, thereby improving the flexibility and accuracy of the reinforced computer hardware power consumption monitoring. Secondly, by analyzing the hardware power consumption energy ratio of each hardware in the reinforced computer based on the power consumption data of the reinforced computer hardware, this analysis process can reveal the power consumption contribution ratio of different hardware in the entire reinforced computer, thereby helping to identify the main sources of energy consumption. By understanding the power consumption energy ratio of each hardware, energy efficiency improvement strategies can be formulated in a targeted manner, focusing on optimizing high-power consumption components, thereby achieving overall energy consumption reduction. By performing workload evaluation and analysis on each hardware in the reinforced computer based on the power consumption data of the reinforced computer hardware and the power consumption energy ratio of the reinforced computer hardware, the workload level of the reinforced computer hardware can be evaluated and analyzed. The purpose of this step is to quantify the power consumption performance of the hardware under different workloads and provide a basis for the dynamic power consumption management of the reinforced computer. By evaluating the workload level of the hardware, the resource allocation of the reinforced computer can be optimized, so that the power consumption characteristics and mutual influence of each hardware component inside the reinforced computer can be fully considered. Then, the workload of the reinforced computer hardware is compared and judged by the preset hardware workload threshold. If the workload of a certain hardware is greater than or equal to the preset threshold, the power consumption mode of the hardware can be determined as a high-load power consumption mode, which can help identify the high power consumption state of each hardware in the reinforced computer, so as to take timely measures to adjust and optimize, and ensure the stability and efficiency of the reinforced computer. When the workload of the reinforced computer hardware is less than the preset hardware workload threshold, the power consumption mode of the hardware can be determined as a low-load power consumption mode, which can adopt corresponding energy-saving strategies to reduce power consumption, such as reducing voltage frequency, adjusting performance mode or shutting down some hardware functions, and at the same time can minimize the energy efficiency of the reinforced computer to extend the service life of the hardware and reduce energy consumption and operating costs.Next, when it is determined that the power consumption mode of the reinforced computer hardware is a high-load power consumption mode, the power consumption intelligent control analysis of each hardware in the reinforced computer is performed based on the power consumption data of the reinforced computer hardware to generate an intelligent control strategy for the high-load power consumption mode. The key to this step is to comprehensively consider the results of dynamic working frequency adjustment and hardware collaborative optimization to formulate an intelligent control strategy, thereby realizing intelligent management and control of the high-load power consumption mode, so that the reinforced computer can maintain a stable power consumption level under high load conditions, while maximizing the performance and energy efficiency of the reinforced computer. Finally, when it is determined that the power consumption mode of the reinforced computer hardware is a low-load power consumption mode, the hardware usage status of each hardware in the reinforced computer is analyzed. This step can comprehensively evaluate the activity level, working status and resource utilization of each hardware, thereby obtaining the hardware usage status data of the reinforced computer. This analysis can not only fully understand the working conditions of each hardware, but also provide data support for the subsequent intelligent sleep trigger condition design, thereby realizing accurate management and control of hardware under low load conditions. In addition, an intelligent sleep strategy response is designed for each hardware in the reinforced computer based on the hardware usage data of the reinforced computer. This step can formulate corresponding intelligent sleep strategies according to different sleep trigger conditions. These strategies may include targeted adjustments to the hardware's working mode, shutting down or reducing unnecessary power consumption modules, optimizing power management and other measures to minimize the energy consumption of the hardware under low load conditions, thereby achieving intelligent optimization and management of the power consumption of the reinforced computer.

[0043] Preferably, step S1 comprises the following steps:

[0044] Monitor the hardware activity status of the reinforced computer to obtain the hardware activity status data of the reinforced computer;

[0045] Based on the hardware activity status data of the reinforced computer, a hardware usage pattern recognition analysis is performed on the reinforced computer to obtain the hardware usage pattern data of the reinforced computer; based on the hardware usage pattern data of the reinforced computer, a hardware usage frequency statistical analysis is performed on the reinforced computer to obtain the hardware usage frequency of the reinforced computer;

[0046] According to the usage frequency of the reinforced computer hardware, the hardware usage efficiency of the reinforced computer is evaluated and analyzed to obtain the usage efficiency of the reinforced computer hardware;

[0047] Obtain the hardware operating temperature of the reinforced computer through the reinforced computer;

[0048] The power consumption of the reinforced computer is monitored and analyzed according to the utilization efficiency and operating temperature of the reinforced computer hardware to obtain the power consumption data of the reinforced computer hardware.

[0049] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0050] S11: monitoring the hardware activity status of the reinforced computer to obtain the hardware activity status data of the reinforced computer;

[0051] The embodiment of the present invention uses an activity status monitoring software program to perform real-time monitoring of various hardware in the reinforced computer (including the motherboard (including CPU+chipset), SSD, cooling fan, LCD screen, battery), so as to monitor the hardware activity status of the reinforced computer in real time, including the working status, usage status and other information of each hardware component in the reinforced computer, and help understand the actual operation status of the reinforced computer, and finally obtain the hardware activity status data of the reinforced computer.

[0052] S12: performing hardware usage pattern recognition analysis on the reinforced computer based on the hardware activity state data of the reinforced computer to obtain hardware usage pattern data of the reinforced computer; performing hardware usage frequency statistical analysis on the reinforced computer based on the hardware usage pattern data of the reinforced computer to obtain the hardware usage frequency of the reinforced computer;

[0053] The embodiment of the present invention uses a method for analyzing the usage patterns of the hardware in the reinforced computer by combining the usage pattern analysis method of the reinforced computer hardware activity state data obtained by monitoring to analyze the association and activity pattern between different hardware activity states, including load level, activity level, usage time, etc., thereby obtaining the usage pattern data of the reinforced computer hardware. Then, by combining the usage pattern data of the reinforced computer hardware obtained by analysis, a statistical analysis of the usage frequency of the corresponding hardware in the reinforced computer is performed to statistically quantify the number of times each hardware component in the reinforced computer is used, and finally the usage frequency of the reinforced computer hardware is obtained.

[0054] S13: performing hardware utilization efficiency evaluation and analysis on the reinforced computer according to the utilization frequency of the reinforced computer hardware to obtain the utilization efficiency of the reinforced computer hardware;

[0055] The embodiment of the present invention performs statistical analysis on the usage efficiency of each corresponding hardware in the reinforced computer by combining the usage frequency of the reinforced computer hardware obtained by analysis, so as to evaluate the effective utilization of the corresponding hardware by combining the hardware usage frequency and the hardware activity status data, and finally obtain the usage efficiency of the reinforced computer hardware.

[0056] S14: obtaining the operating temperature of the hardware of the reinforced computer through the reinforced computer;

[0057] The embodiment of the present invention monitors the surface temperature of each hardware in the reinforced computer in real time by using a temperature sensor to monitor the temperature of the surface of the reinforced computer hardware in real time, and monitors the internal temperature of each hardware in the reinforced computer in real time by using thermal imaging technology or infrared cameras and other equipment to monitor the temperature generated inside the reinforced computer hardware in real time. At the same time, the working temperature of each hardware in the reinforced computer under an ideal working state is determined based on the monitored surface temperature and internal temperature, that is, the ideal working temperature when various heat dissipation measures are not considered to reduce the hardware temperature. Then, the heat dissipation efficiency of the built-in heat dissipation fan of the reinforced computer is obtained through statistical analysis, and the hardware temperature of the reinforced computer hardware corresponding to the determined ideal working temperature is optimized during the heat dissipation process to obtain the actual working temperature of each hardware in the reinforced computer after heat dissipation treatment under normal circumstances, and finally the working temperature of the reinforced computer hardware is obtained.

[0058] S15: Perform power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware to obtain the power consumption data of the reinforced computer hardware.

[0059] The embodiment of the present invention evaluates and analyzes the corresponding hardware in the reinforced computer by combining the analyzed utilization efficiency of the reinforced computer hardware and the working temperature of the reinforced computer hardware and using a working loss statistical analysis method, so as to fully consider the influence of the efficiency and temperature of each hardware on its loss to analyze the loss of each hardware under actual working conditions, and analyze and determine the performance requirements of each hardware, including computing power, storage capacity, response speed and other information. At the same time, the energy efficiency of the corresponding hardware in the reinforced computer is evaluated by combining the performance requirement information of each reinforced computer hardware obtained by analysis, so as to comprehensively consider the performance requirements of the hardware to evaluate the required energy efficiency performance, and fully consider the balance between the energy consumption and performance of the hardware to calculate the energy efficiency performance loss ratio of each hardware. Secondly, the energy efficiency performance loss ratio obtained by the evaluation calculation is used to maximize the working loss of each hardware in the reinforced computer, and then, the working loss obtained after the maximization process is used as the power consumption of each corresponding hardware in the reinforced computer, so as to further improve the energy efficiency performance and energy saving effect of the reinforced computer, and finally obtain the power consumption data of the reinforced computer hardware.

[0060] The present invention firstly monitors the hardware activity status of the reinforced computer, and can obtain the hardware activity status data of the reinforced computer in real time, including the working status, usage and other information of each hardware component in the reinforced computer (including the motherboard (including CPU+chipset), SSD, cooling fan, LCD screen, battery). This monitoring process can help understand the actual operation of the reinforced computer, thereby providing data support for subsequent performance analysis and optimization. Secondly, by performing hardware usage pattern recognition analysis on the reinforced computer based on the hardware activity status data of the reinforced computer, the hardware usage pattern data of the reinforced computer is obtained. This step can deeply understand the working mode of the reinforced computer by analyzing the association and pattern between different hardware activity states, thereby providing a basis for subsequent performance evaluation and optimization. At the same time, by performing a statistical analysis of the hardware usage frequency of the reinforced computer according to the hardware usage pattern data of the reinforced computer, the hardware usage frequency of the reinforced computer can be further analyzed to obtain the hardware usage frequency of the reinforced computer. This analysis can help understand the working frequency and duration of each hardware component in the reinforced computer, thereby providing an important reference for evaluating the working efficiency of the reinforced computer. Then, by evaluating and analyzing the hardware usage efficiency of the reinforced computer according to the usage frequency of the reinforced computer hardware, the hardware usage efficiency of the reinforced computer can be obtained. This evaluation and analysis process can help discover the utilization efficiency of hardware resources and guide subsequent resource allocation and performance optimization work, thereby improving the overall work efficiency and performance of the reinforced computer. Next, by using the reinforced computer to obtain the hardware operating temperature data, the operating temperature of each hardware component of the computer can be monitored in real time. This step helps to understand the thermal operation of the reinforced computer, timely discover and deal with hardware temperature anomalies, thereby providing basic data guarantee for subsequent power consumption monitoring of the reinforced computer. Finally, by monitoring and analyzing the power consumption of the reinforced computer according to the hardware usage efficiency and the hardware operating temperature of the reinforced computer, this analysis can help evaluate the energy consumption of the reinforced computer, guide the subsequent intelligent control of energy saving and emission reduction and resource management, thereby improving the sustainable development of the reinforced computer.

[0061] Preferably, obtaining the hardware operating temperature of the reinforced computer by reinforcing the computer comprises the following steps:

[0062] Use temperature sensors to monitor the hardware surface temperature of the reinforced computer to obtain the real-time temperature of the hardware surface of the reinforced computer;

[0063] Use thermal imaging technology to monitor the internal temperature of the reinforced computer hardware and obtain the real-time internal temperature of the reinforced computer hardware;

[0064] According to the real-time temperature of the surface of the reinforced computer hardware and the real-time temperature inside the reinforced computer hardware, the ideal working temperature fusion analysis of the reinforced computer is performed to obtain the ideal working fusion temperature of the reinforced computer hardware;

[0065] The ideal working fusion temperature of the reinforced computer hardware is optimized by using the built-in cooling fan of the reinforced computer to obtain the working temperature of the reinforced computer hardware.

[0066] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S14 in the embodiment, step S14 includes the following steps:

[0067] S141: Using a temperature sensor to monitor the hardware surface temperature of the reinforced computer to obtain the real-time temperature of the hardware surface of the reinforced computer;

[0068] The embodiment of the present invention uses a temperature sensor to monitor the surface temperature of each hardware in the reinforced computer in real time, so as to monitor the temperature of the surface of the reinforced computer hardware in real time, and effectively evaluate the working status and surface heat distribution of the reinforced computer, and finally obtain the real-time surface temperature of the reinforced computer hardware.

[0069] S142: Using thermal imaging technology to monitor the internal temperature of the hardware of the reinforced computer, and obtaining the real-time internal temperature of the hardware of the reinforced computer;

[0070] The embodiment of the present invention uses thermal imaging technology or infrared cameras and other equipment to monitor the internal temperature of each hardware in the reinforced computer in real time, so as to gain an in-depth understanding of the heat distribution inside the reinforced computer hardware, and monitor the temperature generated inside the reinforced computer hardware in real time, and finally obtain the real-time temperature inside the reinforced computer hardware.

[0071] S143: performing an ideal state working temperature fusion analysis on the reinforced computer according to the real-time temperature of the surface of the reinforced computer hardware and the real-time temperature inside the reinforced computer hardware, so as to obtain an ideal state working fusion temperature of the reinforced computer hardware;

[0072] The embodiment of the present invention integrates the calculation of the working temperature of each corresponding hardware in the reinforced computer by combining the real-time surface temperature of the reinforced computer hardware and the real-time internal temperature of the reinforced computer hardware obtained by real-time monitoring, so as to more accurately determine the working temperature of each hardware in the reinforced computer under the ideal working state, that is, the ideal working temperature without considering the use of various heat dissipation measures to reduce the hardware temperature, and finally obtains the ideal working fusion temperature of the reinforced computer hardware.

[0073] S144: Optimize the temperature of the ideal working fusion temperature of the hardware of the reinforced computer by using a built-in cooling fan of the reinforced computer to obtain the working temperature of the hardware of the reinforced computer.

[0074] The embodiment of the present invention evaluates and analyzes the built-in cooling fan of the reinforced computer by using a performance evaluation method, so as to fully consider the rotation speed, air volume, noise and other parameters of the cooling fan to evaluate the performance information of the cooling fan, and compares the limit heating temperature of each hardware in the reinforced computer by combining the ideal working fusion temperature of the reinforced computer hardware obtained by analysis, so as to screen out the reinforced computer hardware corresponding to the limit heating temperature, and at the same time determine the heat dissipation capacity required by the corresponding computer hardware heat dissipation system, and also intelligently controls the fan speed of the built-in cooling fan of the reinforced computer by combining the heat dissipation demand data obtained by analysis, so as to adjust the fan speed according to the real-time hardware heat dissipation demand. Secondly, by combining the fan performance information obtained by analysis and the intelligent control speed, the common efficiency evaluation and analysis method in the field is used to evaluate and analyze the built-in cooling fan of the reinforced computer in its corresponding heat dissipation process, so as to evaluate and calculate the heat dissipation efficiency of the cooling fan at different rotation speeds, and then, by combining the heat dissipation efficiency of the cooling fan obtained by analysis, the hardware temperature of the reinforced computer hardware corresponding to the ideal working fusion temperature of the reinforced computer hardware is optimized in the heat dissipation process, so as to obtain the actual working temperature of each hardware in the reinforced computer under normal conditions, and finally obtain the working temperature of the reinforced computer hardware.

[0075] The present invention first monitors the hardware surface temperature of the reinforced computer by using a temperature sensor to monitor the temperature of the hardware surface of the reinforced computer in real time. This step can effectively evaluate the working status and surface heat distribution of the reinforced computer by obtaining the temperature data of the hardware surface. This real-time monitoring can help to timely discover existing overheating problems, thereby providing data support for subsequent monitoring of the actual working temperature of the reinforced computer hardware. Secondly, by using thermal imaging technology to monitor the internal temperature of the hardware of the reinforced computer, this step can more comprehensively understand the heat distribution of the computer. Compared with surface temperature monitoring, internal temperature monitoring can more accurately locate heat concentration areas, thereby more effectively optimizing heat dissipation. The application of this technology enables an in-depth understanding of the heat distribution inside the reinforced computer hardware, which helps to timely discover and solve potential problems. Then, an ideal operating temperature fusion analysis of the reinforced computer is performed based on the real-time surface temperature of the reinforced computer hardware and the real-time internal temperature of the reinforced computer hardware. This step can more accurately determine the operating temperature of each hardware in the reinforced computer under ideal working conditions through comprehensive analysis of the hardware surface and internal temperature data, that is, the ideal operating temperature without considering the use of various heat dissipation measures to reduce the hardware temperature. This helps to ensure that the temperature of the hardware of the reinforced computer is within a safe and appropriate range when it is in normal working condition, thereby improving the stability and reliability of the reinforced computer. Finally, the ideal working fusion temperature of the reinforced computer hardware is optimized by the built-in cooling fan of the reinforced computer. This step monitors and optimizes the working temperature of the reinforced computer hardware through the normal use and heat dissipation of the reinforced computer hardware in the working state to ensure that the computer hardware working temperature is within a safe range, thereby obtaining the actual working temperature of each hardware in the reinforced computer under normal conditions. The operation of the cooling fan can effectively reduce the hardware temperature and prevent hardware damage or performance degradation caused by overheating. If the monitored working temperature is not the desired ideal working temperature, it is necessary to optimize the heat dissipation to ensure that the actual working temperature of each hardware under normal conditions can be obtained. This heat dissipation optimization strategy can effectively improve the stability and performance of each hardware in the reinforced computer, extend the service life of the hardware, and provide users with a more reliable computing environment.

[0076] Preferably, the heat dissipation optimization of the ideal working fusion temperature of the reinforced computer hardware by using the built-in heat dissipation fan of the reinforced computer comprises the following steps:

[0077] Perform fan performance evaluation and analysis on the built-in cooling fan of the reinforced computer to obtain performance information data of the cooling fan of the reinforced computer;

[0078] The embodiment of the present invention evaluates and analyzes the built-in cooling fan of the reinforced computer by using a performance evaluation method, so as to fully consider the speed, air volume, noise and other parameters of the cooling fan to evaluate the performance information of the cooling fan, and ensure that the cooling fan has sufficient performance and efficiency during the heat dissipation process, and finally obtain the performance information data of the cooling fan of the reinforced computer.

[0079] Preferably, the hardware heat dissipation requirement analysis of the reinforced computer is performed based on the ideal working fusion temperature of the reinforced computer hardware to obtain the heat dissipation requirement data of the reinforced computer hardware;

[0080] The embodiment of the present invention compares the ultimate heating temperature of each hardware in the reinforced computer by combining the ideal working fusion temperature of the reinforced computer hardware obtained by analysis, so as to screen out the reinforced computer hardware corresponding to the temperature exceeding the ultimate heating temperature, and analyzes the heat dissipation demand of the screened reinforced computer hardware by using the demand analysis method, so as to determine the heat dissipation capacity required by the corresponding computer hardware heat dissipation system, and ensure that the corresponding reinforced computer hardware can effectively dissipate heat under normal working conditions, and finally obtain the heat dissipation demand data of the reinforced computer hardware.

[0081] Preferably, the cooling fan built into the reinforced computer is subjected to intelligent fan control analysis according to the heat dissipation demand data of the reinforced computer hardware, so as to obtain the on-demand intelligent control speed of the cooling fan of the reinforced computer;

[0082] The embodiment of the present invention intelligently controls the fan speed of the built-in cooling fan of the reinforced computer by combining the heat dissipation demand data of the reinforced computer hardware obtained by analysis, so as to design an intelligent control strategy for the cooling fan according to the corresponding heat dissipation demand data of the reinforced computer hardware, and enable the fan speed to be adjusted according to the real-time hardware heat dissipation demand to meet the heat dissipation requirements under different workloads, and finally obtain the on-demand intelligent control speed of the cooling fan of the reinforced computer.

[0083] Preferably, based on the performance information data of the reinforced computer cooling fan and the on-demand intelligent control speed of the reinforced computer cooling fan, the fan cooling efficiency of the built-in cooling fan of the reinforced computer is evaluated and analyzed to obtain the cooling efficiency of the reinforced computer cooling fan;

[0084] The embodiment of the present invention combines the performance information data of the reinforced computer cooling fan obtained by analysis and the on-demand intelligent control of the rotation speed of the reinforced computer cooling fan using the common efficiency evaluation and analysis method in the field to evaluate and analyze the built-in cooling fan of the reinforced computer in its corresponding heat dissipation process, so as to evaluate the heat dissipation efficiency of the cooling fan at different rotation speeds to evaluate its heat dissipation performance under various hardware workloads, and determine its actual effect and performance in the heat dissipation process, and finally obtain the heat dissipation efficiency of the reinforced computer cooling fan.

[0085] Preferably, the ideal operating fusion temperature of the reinforced computer hardware is optimized based on the heat dissipation efficiency of the heat dissipation fan of the reinforced computer to obtain the operating temperature of the reinforced computer hardware.

[0086] The embodiment of the present invention optimizes the hardware temperature of the reinforced computer hardware corresponding to the ideal working fusion temperature of the reinforced computer hardware during the heat dissipation process by combining the heat dissipation efficiency of the reinforced computer heat dissipation fan obtained by analysis, so as to obtain the actual working temperature of each hardware in the reinforced computer under normal circumstances, and finally obtain the working temperature of the reinforced computer hardware.

[0087] The present invention firstly evaluates and analyzes the fan performance of the built-in cooling fan of the reinforced computer to obtain key performance information data. This step can evaluate the performance of the cooling fan to understand its parameters such as air volume, rotation speed, power consumption, etc., so as to provide basic data for subsequent heat dissipation optimization. Such analysis helps to ensure that the cooling fan has sufficient performance and efficiency during the heat dissipation process. Secondly, the hardware heat dissipation demand analysis of the reinforced computer is performed based on the ideal working fusion temperature of the reinforced computer hardware. This step analyzes the temperature demand under the ideal working state of the computer hardware to determine the heat dissipation capacity required by the computer heat dissipation system. Such demand analysis helps to provide guidance for subsequent heat dissipation optimization and ensure that the reinforced computer can effectively dissipate heat under normal working conditions. Then, by performing fan intelligent control analysis on the built-in cooling fan of the reinforced computer according to the heat dissipation demand data of the reinforced computer hardware, the rotation speed of the cooling fan can be adjusted according to the current working state and heat dissipation demand of the computer to achieve the best heat dissipation effect. Such an intelligent control strategy can enable the cooling fan to maintain an appropriate rotation speed under different workloads, thereby improving heat dissipation efficiency and reducing energy consumption. Next, by evaluating and analyzing the cooling efficiency of the built-in cooling fan of the reinforced computer based on the performance information data of the reinforced computer cooling fan and the intelligent speed control of the reinforced computer cooling fan on demand, its actual effect and performance in the cooling process can be determined. Such an evaluation and analysis helps to understand the working status of the cooling fan and provide a reference for further cooling optimization. Finally, by optimizing the ideal working fusion temperature of the reinforced computer hardware based on the cooling efficiency of the reinforced computer cooling fan, this step can optimize the cooling solution, which can ensure that the reinforced computer can maintain the ideal working temperature during the working process, thereby improving its stability and performance. At the same time, such a temperature cooling optimization strategy can effectively reduce the hardware temperature, extend the service life of the hardware, and improve the reliability and performance of the reinforced computer.

[0088] Preferably, the power consumption monitoring and analysis of the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware comprises the following steps:

[0089] Performing hardware work loss evaluation and analysis on each hardware in the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the working temperature of the reinforced computer hardware, so as to obtain the work loss data of each hardware of the reinforced computer;

[0090] The embodiment of the present invention evaluates and analyzes the corresponding hardware in the reinforced computer by combining the analyzed utilization efficiency of the reinforced computer hardware and the working temperature of the reinforced computer hardware and using a working loss statistical analysis method, so as to fully consider the influence of the efficiency and temperature of each hardware on its loss to analyze the loss of each hardware under actual working conditions, and finally obtain the working loss data of each hardware of the reinforced computer.

[0091] Preferably, statistical analysis of performance requirements of each hardware in the reinforced computer is performed to obtain performance requirement information data of each hardware of the reinforced computer;

[0092] The embodiment of the present invention analyzes the corresponding hardware in the reinforced computer by using a performance requirement analysis method to determine the performance requirements of each hardware, including computing power, storage capacity, response speed and other information, and finally obtains performance requirement information data of each reinforced computer hardware.

[0093] Preferably, energy efficiency evaluation and analysis is performed on each hardware in the reinforced computer based on the performance requirement information data of each reinforced computer hardware to obtain energy efficiency performance data of each reinforced computer hardware; performance loss ratio statistical analysis is performed on the work loss data of each reinforced computer hardware according to the energy efficiency performance data of each reinforced computer hardware to obtain the energy efficiency performance loss ratio of each reinforced computer hardware;

[0094] The embodiment of the present invention evaluates and analyzes the energy efficiency of each corresponding hardware in the reinforced computer by combining the performance requirement information data of each reinforced computer hardware obtained through analysis, so as to comprehensively consider the performance requirements of the hardware to evaluate the required energy efficiency performance, thereby obtaining the energy efficiency performance data of each reinforced computer hardware. Then, by combining the energy efficiency performance data of each reinforced computer hardware obtained through analysis, the loss ratio of the working loss data of each reinforced computer hardware is statistically calculated, so as to calculate the energy efficiency performance level of each hardware by considering the balance relationship between the energy consumption and performance of the hardware, and finally obtain the energy efficiency performance loss ratio of each reinforced computer hardware.

[0095] Preferably, the working loss data of each reinforced computer hardware is maximized according to the energy efficiency performance loss ratio of each reinforced computer hardware to obtain the working loss maximization data of each reinforced computer hardware;

[0096] The embodiment of the present invention maximizes the working loss of each reinforced computer hardware by using the energy efficiency performance loss ratio of each reinforced computer hardware obtained by evaluation calculation, so as to maximize the working loss of each hardware in the reinforced computer, and determine whether each hardware achieves the best energy efficiency performance under the premise of meeting the performance requirements, and finally obtains the working loss maximization data of each reinforced computer hardware.

[0097] Preferably, the working loss maximization data of each hardware of the reinforced computer is used as the power consumption of each hardware in the reinforced computer to obtain the power consumption data of the hardware of the reinforced computer.

[0098] The embodiment of the present invention uses the working loss maximization data of each reinforced computer hardware obtained after maximization processing as the power consumption of each corresponding hardware in the reinforced computer, so as to further improve the energy efficiency performance and energy-saving effect of the reinforced computer, and finally obtain the power consumption data of the reinforced computer hardware.

[0099] The present invention firstly evaluates and analyzes the hardware working loss of each hardware in the reinforced computer according to the use efficiency of the reinforced computer hardware and the working temperature of the reinforced computer hardware. This step can quantitatively evaluate the working loss of the hardware by combining the use efficiency and working temperature of the hardware, so as to understand the energy consumption of each hardware. Such evaluation and analysis helps to identify potential energy consumption problems and provide data support for subsequent energy efficiency optimization. Secondly, by statistically analyzing the performance requirements of each hardware in the reinforced computer, the importance and role of each hardware in the reinforced computer system can be understood. Such statistical analysis helps to provide basic data for subsequent energy efficiency evaluation and performance optimization, and ensure that the hardware performance meets the needs of the system. Then, by performing energy efficiency evaluation and analysis on each hardware in the reinforced computer based on the performance requirement information data of each reinforced computer hardware, this step can obtain the energy efficiency performance of each hardware by comprehensively considering the performance requirements and energy efficiency performance data of the hardware. At the same time, by performing performance loss ratio statistical analysis on the working loss data of each reinforced computer hardware according to the energy efficiency performance data of each reinforced computer hardware, the energy efficiency performance loss ratio can be analyzed. Such evaluation and analysis helps to determine the energy efficiency performance level of the hardware, thereby providing data guidance for subsequent energy efficiency optimization. Next, the working loss data of each reinforced computer hardware is maximized according to the energy efficiency performance loss ratio of each reinforced computer hardware to maximize the working loss of each hardware in the reinforced computer. This can determine that each hardware achieves the best energy efficiency performance while meeting the performance requirements. Such analysis helps to optimize the energy consumption performance of the hardware, thereby improving the energy efficiency performance of the reinforced computer. Finally, by using the working loss maximization data of each reinforced computer hardware as the power consumption of each hardware in the reinforced computer, the final hardware power consumption data is obtained. Such data helps to fully understand the energy consumption status of the reinforced computer and provide an important reference for subsequent energy management and performance optimization. This step can take corresponding measures to further improve the energy efficiency performance and energy saving effect of the reinforced computer through the evaluation of the hardware power consumption.

[0100] Preferably, step S2 comprises the following steps:

[0101] Based on the power consumption data of the reinforced computer hardware, the power consumption energy evaluation and analysis of each hardware in the reinforced computer is performed to obtain the power consumption energy of each hardware of the reinforced computer;

[0102] The embodiment of the present invention uses an energy conversion analysis method to analyze the corresponding hardware in the reinforced computer by combining the power consumption data of the reinforced computer hardware obtained by analysis, so as to convert the power consumption of each hardware of the reinforced computer to determine the power consumption energy consumed by each hardware in the reinforced computer, and finally obtain the power consumption energy of each hardware of the reinforced computer.

[0103] Preferably, according to the power consumption energy size of each hardware of the reinforced computer, a statistical analysis of the power consumption energy ratio of each hardware in the reinforced computer is performed to obtain the power consumption energy ratio of the hardware of the reinforced computer;

[0104] The embodiment of the present invention uses the power consumption energy size of each reinforced computer hardware obtained by analysis to perform statistical analysis on the energy proportion of each corresponding hardware in the reinforced computer, so as to statistically calculate the energy proportion of each hardware in the overall power consumption, and reveal the power consumption contribution ratio of different hardware in the entire reinforced computer, and finally obtain the power consumption energy proportion of the reinforced computer hardware.

[0105] Preferably, based on the power consumption data of the reinforced computer hardware and the power consumption energy proportion of the reinforced computer hardware, the workload power consumption impact mining analysis is performed on each hardware in the reinforced computer to obtain the impact correlation relationship between the power consumption of the reinforced computer hardware and the workload;

[0106] The embodiment of the present invention analyzes the corresponding hardware in the reinforced computer by combining the power consumption data of the reinforced computer hardware and the energy proportion of the power consumption of the reinforced computer hardware obtained by analysis, so as to mine and analyze the influence correlation relationship between the power consumption of the hardware and the workload, and explore the power consumption change law of the hardware under different workloads, and finally obtain the influence correlation relationship between the power consumption of the reinforced computer hardware and the workload.

[0107] Preferably, workload evaluation and analysis is performed on each hardware in the reinforced computer according to the influence correlation between the power consumption of the reinforced computer hardware and the workload, so as to obtain the workload degree of the reinforced computer hardware.

[0108] The embodiment of the present invention uses a load evaluation algorithm to evaluate and analyze the corresponding hardware in the reinforced computer by combining the analyzed influence correlation relationship between the power consumption of the reinforced computer hardware and the workload, so as to fully consider the power consumption characteristics and mutual influence relationship of each hardware component inside the reinforced computer to quantify the power consumption performance of the hardware under different workloads, and finally obtain the workload degree of the reinforced computer hardware.

[0109] The present invention firstly performs a power consumption energy evaluation and analysis on each hardware in the reinforced computer based on the power consumption data of the reinforced computer hardware, and can determine the power consumption energy consumed by each hardware in the reinforced computer. This evaluation and analysis process is crucial to revealing the energy consumption characteristics of each hardware, and helps to identify energy consumption hotspots and potential energy-saving optimization opportunities in the reinforced computer. By obtaining the power consumption energy data of each hardware, basic data and direction can be provided for subsequent energy efficiency improvements. Secondly, by performing a statistical analysis of the power consumption energy proportion of each hardware in the reinforced computer according to the power consumption energy size of each reinforced computer hardware, this analysis and processing process can reveal the power consumption contribution ratio of different hardware in the entire reinforced computer, thereby helping to identify the main sources of energy consumption. By understanding the power consumption energy proportion of each hardware, energy efficiency improvement strategies can be formulated in a targeted manner, focusing on optimizing high-power consumption components, thereby achieving overall energy consumption reduction. Then, based on the power consumption data of the reinforced computer hardware and the proportion of the power consumption of the reinforced computer hardware, the workload power consumption impact mining analysis is performed on each hardware in the reinforced computer to explore and analyze the correlation between the power consumption of the reinforced computer hardware and the workload. This analysis can help understand the power consumption changes of the hardware under different workloads to identify the impact of the workload on the energy efficiency of the reinforced computer. By mining this correlation, the hardware usage strategy can be optimized to minimize power consumption and improve energy efficiency without affecting performance. Finally, by performing workload evaluation analysis on each hardware in the reinforced computer according to the impact correlation between the power consumption of the reinforced computer hardware and the workload, the workload degree of the reinforced computer hardware is evaluated and analyzed. The purpose of this step is to quantify the power consumption performance of the hardware under different workloads and provide a basis for the dynamic power consumption management of the reinforced computer. By evaluating the workload degree of the hardware, the resource allocation of the reinforced computer can be optimized, so that the power consumption characteristics and mutual influence relationship of each hardware component inside the reinforced computer can be fully considered.

[0110] Preferably, the comparing and judging the hardware power consumption mode of the reinforced computer according to the workload degree of the reinforced computer hardware includes the following steps:

[0111] The hardware workload degree of the reinforced computer is compared and judged according to a preset hardware workload threshold value. When the hardware workload degree of the reinforced computer is greater than or equal to the preset hardware workload threshold value, the hardware power consumption mode of the reinforced computer corresponding to the hardware workload degree of the reinforced computer is judged as a high-load power consumption mode;

[0112] The embodiment of the present invention compares and judges the workload degree of the reinforced computer hardware obtained by evaluation and calculation by using a preset hardware workload threshold. If the workload degree of the reinforced computer hardware is greater than or equal to the preset hardware workload threshold, it means that the workload degree of each reinforced computer hardware corresponding to the workload degree of the reinforced computer hardware is large, and the hardware power consumption mode of the reinforced computer corresponding to the hardware workload degree of the reinforced computer is judged as a high-load power consumption mode.

[0113] Preferably, when the hardware workload level of the reinforced computer is less than a preset hardware workload threshold, the hardware power consumption mode of the reinforced computer corresponding to the hardware workload level of the reinforced computer is determined to be a low-load power consumption mode.

[0114] If the embodiment of the present invention determines that the workload level of the reinforced computer hardware is less than a preset hardware workload threshold, it means that the workload levels of each reinforced computer hardware corresponding to the workload level of the reinforced computer hardware are small, then the hardware power consumption mode of the reinforced computer corresponding to the workload level of the reinforced computer hardware is judged as a low-load power consumption mode.

[0115] The present invention first compares and judges the workload degree of the reinforced computer hardware through a preset hardware workload threshold. If the workload degree of a certain hardware is greater than or equal to the preset threshold, the power consumption mode of the hardware can be judged as a high-load power consumption mode. This discrimination process is crucial for real-time monitoring and responding to system load changes, and can help identify the high power consumption state of each hardware in the reinforced computer, so as to take timely measures to adjust and optimize, and ensure the stability and efficiency of the reinforced computer. When the workload degree of the reinforced computer hardware is less than the preset hardware workload threshold, the power consumption mode of the hardware can be judged as a low-load power consumption mode. This discrimination process helps to determine that the reinforced computer is in a light-load state, and corresponding energy-saving strategies can be adopted to reduce power consumption, such as reducing voltage frequency, adjusting performance mode or shutting down some hardware functions. By timely identifying the low-load state, the energy efficiency of the reinforced computer can be minimized to extend the service life of the hardware, while reducing energy consumption and operating costs.

[0116] Preferably, step S4 comprises the following steps:

[0117] When it is determined that the power consumption mode of the hardware of the reinforced computer is a high-load power consumption mode, a load power consumption target is designed for each hardware in the reinforced computer based on the power consumption data of the hardware of the reinforced computer, so as to obtain a load power consumption target of the hardware of the reinforced computer;

[0118] When the embodiment of the present invention determines that the power consumption mode of the reinforced computer hardware is a high-load power consumption mode, the load power consumption target of each corresponding hardware in the reinforced computer is designed by combining the power consumption data of the reinforced computer hardware obtained by analysis, so as to analyze the power consumption of each hardware according to the current high-load state, set a reasonable power consumption target for each hardware, and ensure the working stability and performance of each hardware under high-load conditions, and finally obtain the load power consumption target of the reinforced computer hardware.

[0119] Preferably, dynamic operating frequency adjustment processing is performed on each hardware in the reinforced computer based on the load power consumption target of the reinforced computer hardware to obtain dynamic operating frequency adjustment measures for the reinforced computer hardware;

[0120] The embodiment of the present invention dynamically adjusts the operating frequency of each corresponding hardware in the reinforced computer by combining the determined load power consumption target of the reinforced computer hardware, so as to generate various measures that can adjust the operating frequency of the hardware device according to the current load power consumption target and the current working state of each hardware, including dynamically adjusting the operating frequency of the processor, CPU and GPU, hard disk, etc., and ensuring that the power consumption of each hardware can be reduced to the greatest extent while maintaining the performance and stability of the reinforced computer, and finally obtaining dynamic operating frequency adjustment measures for the reinforced computer hardware.

[0121] Preferably, a hardware dynamic collaborative optimization analysis is performed on each hardware in the reinforced computer based on the load power consumption target of the reinforced computer hardware to obtain a dynamic collaborative optimization measure for the reinforced computer hardware;

[0122] The embodiment of the present invention performs dynamic collaborative optimization analysis on the corresponding hardware in the reinforced computer by combining the determined load power consumption target of the reinforced computer hardware to generate various measures that can optimize the working state of each hardware according to the current load power consumption target and the current working state of each hardware, including measures such as optimizing the hard disk rotation speed, optimizing memory usage, optimizing voltage supply, etc., and ensure that the reinforced computer can achieve higher energy efficiency and performance under high load conditions, and finally obtain dynamic collaborative optimization measures for the reinforced computer hardware.

[0123] Preferably, according to the dynamic operating frequency adjustment measures of the reinforced computer hardware and the dynamic collaborative optimization measures of the reinforced computer hardware, the power consumption intelligent control analysis of each hardware in the reinforced computer is performed, and a high-load power consumption mode intelligent control strategy is generated to execute the corresponding hardware high-load power consumption intelligent control work.

[0124] The embodiment of the present invention performs intelligent control analysis on the corresponding hardware in the reinforced computer by integrating and analyzing the dynamic operating frequency adjustment measures of the reinforced computer hardware and the dynamic collaborative optimization measures of the reinforced computer hardware, so as to determine the working mode and adjustment scheme of each hardware device under high load conditions, and enable it to realize intelligent control of hardware high load power consumption, and finally generate a high load power consumption mode intelligent control strategy to perform corresponding hardware high load power consumption intelligent control work, including dynamically adjusting the operating frequency of the processor, CPU and GPU, adjusting the rotation speed of the hard disk, reducing the power supply voltage of peripherals, optimizing memory usage and other measures.

[0125] The present invention firstly designs the load power consumption target of each hardware in the reinforced computer based on the power consumption data of the reinforced computer hardware when the power consumption mode of the reinforced computer hardware is determined to be a high-load power consumption mode. In this way, the power consumption of each hardware can be analyzed for the current high-load state, so as to set a reasonable power consumption target for each hardware. This process can not only effectively control the overall power consumption level, but also balance the energy distribution between the hardware inside the reinforced computer, ensuring that the reinforced computer runs in a stable and efficient state. Secondly, the dynamic working frequency adjustment process is performed on each hardware in the reinforced computer based on the load power consumption target of the reinforced computer hardware. The key of this step is that the working frequency of the hardware can be dynamically adjusted according to the set power consumption target to reduce the power consumption without affecting the performance of the reinforced computer. By real-time monitoring and adjusting the working frequency of the hardware, it is possible to respond to load changes more flexibly, thereby realizing intelligent optimization of power consumption. Then, the hardware dynamic collaborative optimization analysis is performed on each hardware in the reinforced computer based on the load power consumption target of the reinforced computer hardware. The key of this step is that the working state and energy consumption of each hardware are collaboratively optimized, so that the reinforced computer can achieve higher energy efficiency ratio and performance performance under high load. Through dynamic collaborative optimization, resources can be used more effectively, energy consumption can be reduced, hardware life can be extended, and the stability and reliability of the overall reinforced computer can be improved. Finally, the power consumption of each hardware is intelligently controlled and analyzed according to the dynamic working frequency adjustment measures and hardware dynamic collaborative optimization measures to generate an intelligent control strategy for high-load power consumption mode. The key to this step is to comprehensively consider the results of dynamic working frequency adjustment and hardware collaborative optimization to formulate an intelligent control strategy, thereby realizing intelligent management and control of high-load power consumption mode, so that the reinforced computer can maintain a stable power consumption level under high load conditions, while maximizing the performance and energy efficiency of the reinforced computer.

[0126] Preferably, step S5 comprises the following steps:

[0127] When it is determined that the power consumption mode of the hardware of the reinforced computer is a low-load power consumption mode, a hardware usage status analysis is performed on each hardware in the reinforced computer to obtain the hardware usage status data of the reinforced computer;

[0128] When the embodiment of the present invention determines that the power consumption mode of the reinforced computer hardware is judged to be a low-load power consumption mode, the usage status of each hardware in the reinforced computer is analyzed to monitor and analyze the activity level, working status and resource utilization of each hardware in the reinforced computer, and finally obtain the usage status data of the reinforced computer hardware.

[0129] Preferably, intelligent sleep triggering conditions are designed for each hardware in the reinforced computer according to the usage status data of the reinforced computer hardware, so as to generate intelligent sleep triggering conditions for the reinforced computer hardware;

[0130] The embodiment of the present invention designs the triggering of the sleep mode of each corresponding hardware in the reinforced computer by combining the analyzed usage status data of the reinforced computer hardware, so as to set the triggering conditions such as the threshold of the corresponding hardware usage rate and the device idle time threshold based on factors such as the hardware idle time, task completion status, and energy consumption level, so as to enable it to flexibly trigger the intelligent sleep mode of the hardware, and at the same time effectively reduce the hardware energy consumption under low load conditions, and finally generate the intelligent sleep triggering conditions of the reinforced computer hardware.

[0131] Preferably, based on the intelligent sleep triggering conditions of the reinforced computer hardware, an intelligent sleep strategy response design is performed on each hardware in the reinforced computer, and a low-load power consumption mode intelligent sleep strategy is generated to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0132] The embodiment of the present invention responds to and processes the corresponding hardware in the reinforced computer by generating intelligent sleep trigger conditions for the reinforced computer hardware according to the design, so as to formulate corresponding hardware intelligent sleep strategies that meet the trigger conditions in the reinforced computer according to different sleep trigger condition responses, including targeted adjustment of the hardware working mode, shutting down or reducing unnecessary power consumption modules, optimizing power management and other measures, and ensuring that the energy consumption of the hardware in a low-load state is reduced to the greatest extent, and finally generating a low-load power consumption mode intelligent sleep strategy to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0133] The present invention first analyzes the hardware usage status of each hardware in the reinforced computer when it is determined that the power consumption mode of the reinforced computer hardware is judged as a low-load power consumption mode. This step can obtain the hardware usage status data of the reinforced computer by comprehensively evaluating the activity level, working status and resource utilization of each hardware. This analysis can not only fully understand the working status of each hardware, but also provide data support for the subsequent design of intelligent sleep trigger conditions, thereby realizing accurate management and control of the hardware under low-load conditions. Then, by designing intelligent sleep trigger conditions for each hardware in the reinforced computer according to the hardware usage status data of the reinforced computer, these conditions can flexibly trigger the intelligent sleep mode of the hardware based on factors such as the idle time of the hardware, the completion of the task, and the energy consumption level, thereby effectively reducing the hardware energy consumption under low-load conditions, while ensuring that the response speed and performance of the reinforced computer are not affected. Finally, by designing intelligent sleep strategy responses for each hardware in the reinforced computer based on the intelligent sleep trigger conditions of the reinforced computer hardware, this step can formulate corresponding intelligent sleep strategies according to different sleep trigger conditions. These strategies can include targeted adjustments to the hardware's working mode, shutting down or reducing unnecessary power consumption modules, optimizing power management and other measures to minimize the energy consumption of the hardware under low load conditions, thereby achieving intelligent optimization and management of the power consumption of the reinforced computer.

[0134] Preferably, the present invention further provides an intelligent power consumption control device for a reinforced computer, which is used to execute the intelligent power consumption control method for a reinforced computer as described above, and the intelligent power consumption control device for a reinforced computer comprises:

[0135] The power consumption monitoring module of the reinforced computer hardware is used to obtain the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware, and perform power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware, so as to obtain the power consumption data of the reinforced computer hardware;

[0136] A reinforcement computer workload assessment module is used to analyze the hardware power consumption energy ratio of the reinforcement computer based on the power consumption data of the reinforcement computer hardware, so as to obtain the power consumption energy ratio of the reinforcement computer hardware; based on the power consumption data of the reinforcement computer hardware and the power consumption energy ratio of the reinforcement computer hardware, a workload assessment analysis is performed on the reinforcement computer, so as to obtain the degree of the reinforcement computer hardware workload;

[0137] The reinforcement computer hardware power consumption mode judgment module is used to compare and judge the hardware power consumption mode of the reinforcement computer according to the workload of the reinforcement computer hardware to obtain the reinforcement computer hardware power consumption mode judgment result, wherein the reinforcement computer hardware power consumption mode judgment result includes a high load power consumption mode and a low load power consumption mode;

[0138] The hardware high-load power consumption mode control module is used to determine that when the power consumption mode judgment result of the reinforced computer hardware is a high-load power consumption mode, perform power consumption intelligent control analysis on the reinforced computer based on the power consumption data of the reinforced computer hardware, generate a high-load power consumption mode intelligent control strategy, and execute the corresponding hardware high-load power consumption intelligent control work;

[0139] The hardware low-load power consumption mode control module is used to determine that when the hardware power consumption mode of the reinforced computer is judged as the low-load power consumption mode, the hardware usage status of the reinforced computer is analyzed to obtain the hardware usage status data of the reinforced computer; an intelligent sleep strategy is designed according to the hardware usage status data of the reinforced computer to execute the corresponding hardware low-load power consumption intelligent sleep work.

[0140] In summary, the present invention provides an intelligent power consumption control device for a reinforced computer, which is composed of a reinforced computer hardware power consumption monitoring module, a reinforced computer workload evaluation module, a reinforced computer hardware power consumption mode judgment module, a hardware high-load power consumption mode control module, and a hardware low-load power consumption mode control module. It can implement any of the intelligent power consumption control methods for a reinforced computer described in the present invention, and is used to combine the operations between computer programs running on each module to implement any of the intelligent power consumption control methods for a reinforced computer. The internal structures of the device cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient intelligent power consumption control process, thereby simplifying the operating procedures of the intelligent power consumption control device for a reinforced computer.

[0141] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0142] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for intelligently controlling power consumption of a reinforced computer, characterized in that: The following steps are involved: Step S1: obtaining the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware through the reinforced computer, and performing power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware to obtain the power consumption data of the reinforced computer hardware; wherein step S1 includes the following steps: Monitor the hardware activity status of the reinforced computer to obtain the hardware activity status data of the reinforced computer; Based on the hardware activity status data of the reinforced computer, a hardware usage pattern recognition analysis is performed on the reinforced computer to obtain the hardware usage pattern data of the reinforced computer; based on the hardware usage pattern data of the reinforced computer, a hardware usage frequency statistical analysis is performed on the reinforced computer to obtain the hardware usage frequency of the reinforced computer; According to the usage frequency of the reinforced computer hardware, the hardware usage efficiency of the reinforced computer is evaluated and analyzed to obtain the usage efficiency of the reinforced computer hardware; Obtaining the hardware operating temperature of the reinforced computer through the reinforced computer; wherein the steps include: Use temperature sensors to monitor the hardware surface temperature of the reinforced computer to obtain the real-time temperature of the hardware surface of the reinforced computer; Use thermal imaging technology to monitor the internal temperature of the reinforced computer hardware and obtain the real-time internal temperature of the reinforced computer hardware; According to the real-time temperature of the surface of the reinforced computer hardware and the real-time temperature inside the reinforced computer hardware, the ideal working temperature fusion analysis of the reinforced computer is performed to obtain the ideal working fusion temperature of the reinforced computer hardware; The heat dissipation optimization is performed on the ideal working fusion temperature of the reinforced computer hardware by using the built-in heat dissipation fan of the reinforced computer to obtain the working temperature of the reinforced computer hardware; wherein the heat dissipation optimization includes the following steps: Perform fan performance evaluation and analysis on the built-in cooling fan of the reinforced computer to obtain performance information data of the cooling fan of the reinforced computer; Based on the ideal working fusion temperature of the reinforced computer hardware, the hardware heat dissipation demand of the reinforced computer is analyzed to obtain the heat dissipation demand data of the reinforced computer hardware; According to the heat dissipation demand data of the reinforced computer hardware, the built-in cooling fan of the reinforced computer is subjected to intelligent control analysis to obtain the on-demand intelligent control speed of the cooling fan of the reinforced computer; Based on the performance information data of the reinforced computer cooling fan and the on-demand intelligent control speed of the reinforced computer cooling fan, the fan cooling efficiency of the reinforced computer built-in cooling fan is evaluated and analyzed to obtain the cooling efficiency of the reinforced computer cooling fan; Based on the heat dissipation efficiency of the heat dissipation fan of the reinforced computer, the temperature heat dissipation optimization is performed on the ideal working fusion temperature of the reinforced computer hardware to obtain the working temperature of the reinforced computer hardware; The power consumption data of the reinforced computer hardware is obtained by monitoring and analyzing the power consumption of the reinforced computer hardware according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware; wherein the power consumption monitoring and analysis comprises the following steps: Performing hardware work loss evaluation and analysis on each hardware in the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the working temperature of the reinforced computer hardware, so as to obtain the work loss data of each hardware of the reinforced computer; Perform statistical analysis on the performance requirements of each hardware in the reinforced computer to obtain performance requirement information data of each hardware of the reinforced computer; Based on the performance requirement information data of each reinforced computer hardware, energy efficiency evaluation and analysis are performed on each hardware in the reinforced computer to obtain energy efficiency performance data of each reinforced computer hardware; based on the energy efficiency performance data of each reinforced computer hardware, performance loss ratio statistical analysis is performed on the work loss data of each reinforced computer hardware to obtain the energy efficiency performance loss ratio of each reinforced computer hardware; According to the energy efficiency performance loss ratio of each reinforced computer hardware, the working loss data of each reinforced computer hardware is maximized to obtain the working loss maximization data of each reinforced computer hardware; The work loss maximization data of each reinforced computer hardware is used as the power consumption of each hardware in the reinforced computer to obtain the power consumption data of the reinforced computer hardware; Step S2: Based on the power consumption data of the reinforced computer hardware, the hardware power consumption energy ratio of the reinforced computer is analyzed to obtain the power consumption energy ratio of the reinforced computer hardware; Based on the power consumption data of the reinforced computer hardware and the power consumption energy ratio of the reinforced computer hardware, the workload evaluation analysis of the reinforced computer is performed to obtain the workload degree of the reinforced computer hardware; Step S3: comparing and judging the hardware power consumption mode of the reinforced computer according to the workload of the reinforced computer hardware to obtain a judgment result of the hardware power consumption mode of the reinforced computer, wherein the judgment result of the hardware power consumption mode of the reinforced computer includes a high load power consumption mode and a low load power consumption mode; Step S4: when it is determined that the power consumption mode of the hardware of the reinforced computer is a high-load power consumption mode, the power consumption intelligent control analysis of the reinforced computer is performed based on the power consumption data of the hardware of the reinforced computer, and a high-load power consumption mode intelligent control strategy is generated to perform the corresponding hardware high-load power consumption intelligent control work; wherein, step S4 includes the following steps: When it is determined that the power consumption mode of the hardware of the reinforced computer is a high-load power consumption mode, a load power consumption target is designed for each hardware in the reinforced computer based on the power consumption data of the hardware of the reinforced computer, so as to obtain a load power consumption target of the hardware of the reinforced computer; Based on the load power consumption target of the reinforced computer hardware, dynamic operating frequency adjustment processing is performed on each hardware in the reinforced computer to obtain dynamic operating frequency adjustment measures for the reinforced computer hardware; Based on the power consumption target of the reinforced computer hardware load, the hardware dynamic collaborative optimization analysis is performed on each hardware in the reinforced computer, and the dynamic collaborative optimization measures of the reinforced computer hardware are obtained; According to the dynamic working frequency adjustment measures of the reinforced computer hardware and the dynamic collaborative optimization measures of the reinforced computer hardware, the power consumption intelligent control analysis of each hardware in the reinforced computer is performed to generate a high-load power consumption mode intelligent control strategy to perform the corresponding hardware high-load power consumption intelligent control work; Step S5: When it is determined that the hardware power consumption mode of the reinforced computer is in low-load power consumption mode, the hardware usage status of the reinforced computer is analyzed to obtain the hardware usage status data of the reinforced computer; an intelligent sleep strategy is designed according to the hardware usage status data of the reinforced computer to execute the corresponding hardware low-load power consumption intelligent sleep work.

2. The method for intelligently controlling power consumption of a reinforced computer according to claim 1, characterized in that: Step S2 includes the following steps: Based on the power consumption data of the reinforced computer hardware, the power consumption energy evaluation and analysis of each hardware in the reinforced computer is performed to obtain the power consumption energy of each hardware of the reinforced computer; According to the power consumption energy of each reinforced computer hardware, statistical analysis is performed on the power consumption energy ratio of each hardware in the reinforced computer to obtain the power consumption energy ratio of the reinforced computer hardware; Based on the power consumption data of the reinforced computer hardware and the proportion of the power consumption energy of the reinforced computer hardware, the workload power consumption impact mining and analysis of each hardware in the reinforced computer is carried out to obtain the impact correlation relationship between the power consumption of the reinforced computer hardware and the workload; According to the influence correlation between the power consumption of the reinforced computer hardware and the workload, the workload evaluation and analysis of each hardware in the reinforced computer is performed to obtain the workload degree of the reinforced computer hardware.

3. The intelligent power consumption control method for strengthening a computer according to claim 1, characterized in that: The step of comparing and judging the hardware power consumption mode of the reinforced computer according to the workload degree of the reinforced computer hardware comprises the following steps: The hardware workload degree of the reinforced computer is compared and judged according to a preset hardware workload threshold value. When the hardware workload degree of the reinforced computer is greater than or equal to the preset hardware workload threshold value, the hardware power consumption mode of the reinforced computer corresponding to the hardware workload degree of the reinforced computer is judged as a high-load power consumption mode; When the workload level of the reinforced computer hardware is less than a preset hardware workload threshold, the hardware power consumption mode of the reinforced computer corresponding to the workload level of the reinforced computer is determined to be a low-load power consumption mode.

4. The method for intelligently controlling power consumption of a reinforced computer according to claim 1, characterized in that: Step S5 includes the following steps: When it is determined that the power consumption mode of the hardware of the reinforced computer is a low-load power consumption mode, a hardware usage status analysis is performed on each hardware in the reinforced computer to obtain the hardware usage status data of the reinforced computer; Design intelligent sleep trigger conditions for each hardware in the reinforced computer according to the usage status data of the reinforced computer hardware to generate intelligent sleep trigger conditions for the reinforced computer hardware; Based on the intelligent sleep trigger conditions of the reinforced computer hardware, an intelligent sleep strategy response design is performed for each hardware in the reinforced computer, and a low-load power consumption mode intelligent sleep strategy is generated to execute the corresponding hardware low-load power consumption intelligent sleep work.

5. An intelligent power consumption control device for strengthening a computer, characterized in that: The method for intelligently controlling power consumption of a reinforced computer according to claim 1 is used to execute the method, wherein the intelligent power consumption control device for a reinforced computer comprises: The power consumption monitoring module of the reinforced computer hardware is used to obtain the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware, and perform power consumption monitoring and analysis on the reinforced computer according to the utilization efficiency of the reinforced computer hardware and the operating temperature of the reinforced computer hardware, so as to obtain the power consumption data of the reinforced computer hardware; A reinforcement computer workload assessment module is used to analyze the hardware power consumption energy ratio of the reinforcement computer based on the power consumption data of the reinforcement computer hardware, so as to obtain the power consumption energy ratio of the reinforcement computer hardware; based on the power consumption data of the reinforcement computer hardware and the power consumption energy ratio of the reinforcement computer hardware, a workload assessment analysis is performed on the reinforcement computer, so as to obtain the degree of the reinforcement computer hardware workload; The reinforcement computer hardware power consumption mode judgment module is used to compare and judge the hardware power consumption mode of the reinforcement computer according to the workload of the reinforcement computer hardware to obtain the reinforcement computer hardware power consumption mode judgment result, wherein the reinforcement computer hardware power consumption mode judgment result includes a high load power consumption mode and a low load power consumption mode; The hardware high-load power consumption mode control module is used to determine that when the judgment result of the power consumption mode of the reinforced computer hardware is a high-load power consumption mode, the load power consumption target design is performed on each hardware in the reinforced computer based on the power consumption data of the reinforced computer hardware to obtain the load power consumption target of the reinforced computer hardware; the dynamic working frequency adjustment processing is performed on each hardware in the reinforced computer based on the load power consumption target of the reinforced computer hardware to obtain the dynamic working frequency adjustment measures of the reinforced computer hardware; the hardware dynamic collaborative optimization analysis is performed on each hardware in the reinforced computer based on the load power consumption target of the reinforced computer hardware to obtain the dynamic collaborative optimization measures of the reinforced computer hardware; the power consumption intelligent control analysis is performed on each hardware in the reinforced computer according to the dynamic working frequency adjustment measures of the reinforced computer hardware and the dynamic collaborative optimization measures of the reinforced computer hardware, and a high-load power consumption mode intelligent control strategy is generated to execute the corresponding hardware high-load power consumption intelligent control work; The hardware low-load power consumption mode control module is used to determine that when the hardware power consumption mode of the reinforced computer is judged as the low-load power consumption mode, the hardware usage status of the reinforced computer is analyzed to obtain the hardware usage status data of the reinforced computer; an intelligent sleep strategy is designed according to the hardware usage status data of the reinforced computer to execute the corresponding hardware low-load power consumption intelligent sleep work.

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